# HG changeset patch # User bgruening # Date 1539283074 14400 # Node ID 4fac53da862f1beda44b54b7a5edceadfd71ad86 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88 diff -r 000000000000 -r 4fac53da862f plot_ml_performance.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plot_ml_performance.py Thu Oct 11 14:37:54 2018 -0400 @@ -0,0 +1,152 @@ +import argparse +import pandas as pd +import plotly +import pickle +import plotly.graph_objs as go +from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, roc_curve, auc +from sklearn.preprocessing import label_binarize + + +def main(infile_input, infile_output, infile_trained_model): + """ + Produce an interactive confusion matrix (heatmap), precision, recall, fscore and auc plots + Args: + infile_input: str, input tabular file with true labels + infile_output: str, input tabular file with predicted labels + infile_trained_model: str, input trained model file (zip) + """ + + df_input = pd.read_csv(infile_input, sep='\t', parse_dates=True) + df_output = pd.read_csv(infile_output, sep='\t', parse_dates=True) + true_labels = df_input.iloc[:, -1].copy() + predicted_labels = df_output.iloc[:, -1].copy() + axis_labels = list(set(true_labels)) + c_matrix = confusion_matrix(true_labels, predicted_labels) + data = [ + go.Heatmap( + z=c_matrix, + x=axis_labels, + y=axis_labels, + colorscale='Portland', + ) + ] + + layout = go.Layout( + title='Confusion Matrix between true and predicted class labels', + xaxis=dict(title='True class labels'), + yaxis=dict(title='Predicted class labels') + ) + + fig = go.Figure(data=data, layout=layout) + plotly.offline.plot(fig, filename="output_confusion.html", auto_open=False) + + # plot precision, recall and f_score for each class label + precision, recall, f_score, _ = precision_recall_fscore_support(true_labels, predicted_labels) + + trace_precision = go.Scatter( + x=axis_labels, + y=precision, + mode='lines+markers', + name='Precision' + ) + + trace_recall = go.Scatter( + x=axis_labels, + y=recall, + mode='lines+markers', + name='Recall' + ) + + trace_fscore = go.Scatter( + x=axis_labels, + y=f_score, + mode='lines+markers', + name='F-score' + ) + + layout_prf = go.Layout( + title='Precision, recall and f-score of true and predicted class labels', + xaxis=dict(title='Class labels'), + yaxis=dict(title='Precision, recall and f-score') + ) + + data_prf = [trace_precision, trace_recall, trace_fscore] + fig_prf = go.Figure(data=data_prf, layout=layout_prf) + plotly.offline.plot(fig_prf, filename="output_prf.html", auto_open=False) + + # plot roc and auc curves for different classes + with open(infile_trained_model, 'rb') as model_file: + model = pickle.load(model_file) + + # remove the last column (label column) + test_data = df_input.iloc[:, :-1] + model_items = dir(model) + + try: + # find the probability estimating method + if 'predict_proba' in model_items: + y_score = model.predict_proba(test_data) + elif 'decision_function' in model_items: + y_score = model.decision_function(test_data) + + true_labels_list = true_labels.tolist() + one_hot_labels = label_binarize(true_labels_list, classes=axis_labels) + data_roc = list() + + if len(axis_labels) > 2: + fpr = dict() + tpr = dict() + roc_auc = dict() + for i in axis_labels: + fpr[i], tpr[i], _ = roc_curve(one_hot_labels[:, i], y_score[:, i]) + roc_auc[i] = auc(fpr[i], tpr[i]) + for i in range(len(axis_labels)): + trace = go.Scatter( + x=fpr[i], + y=tpr[i], + mode='lines+markers', + name='ROC curve of class {0} (AUC = {1:0.2f})'.format(i, roc_auc[i]) + ) + data_roc.append(trace) + else: + try: + y_score_binary = y_score[:, 1] + except: + y_score_binary = y_score + fpr, tpr, _ = roc_curve(one_hot_labels, y_score_binary, pos_label=1) + roc_auc = auc(fpr, tpr) + trace = go.Scatter( + x=fpr, + y=tpr, + mode='lines+markers', + name='ROC curve (AUC = {0:0.2f})'.format(roc_auc) + ) + data_roc.append(trace) + + trace_diag = go.Scatter( + x=[0, 1], + y=[0, 1], + mode='lines', + name='Chance' + ) + data_roc.append(trace_diag) + layout_roc = go.Layout( + title='Receiver operating characteristics (ROC) and area under curve (AUC)', + xaxis=dict(title='False positive rate'), + yaxis=dict(title='True positive rate') + ) + + fig_roc = go.Figure(data=data_roc, layout=layout_roc) + plotly.offline.plot(fig_roc, filename="output_roc.html", auto_open=False) + + except Exception as exp: + pass + + +if __name__ == "__main__": + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--input", dest="infile_input", required=True) + aparser.add_argument("-j", "--output", dest="infile_output", required=True) + aparser.add_argument("-k", "--model", dest="infile_trained_model", required=True) + args = aparser.parse_args() + main(args.infile_input, args.infile_output, args.infile_trained_model) diff -r 000000000000 -r 4fac53da862f plotly_ml_performance_plots.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plotly_ml_performance_plots.xml Thu Oct 11 14:37:54 2018 -0400 @@ -0,0 +1,86 @@ + + of tabular data + + pandas + plotly + scikit-learn + scipy + + echo $version + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + `_ from tabular files. The input data contains the original/true class labels (last column) and the predicted data contains the predicted class labels (last column). The true and predicted class labels are plotted against each other. The diagonal of this heatmap shows the correctly predicted data. The plot is buried in a html file which +provides rich interactive features. Image can be saved in various format, such as 'png', 'svg', 'jpeg' and so on. + + + ]]> + + diff -r 000000000000 -r 4fac53da862f test-data/binary_prediction_sgd.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/binary_prediction_sgd.tabular Thu Oct 11 14:37:54 2018 -0400 @@ -0,0 +1,801 @@ +-0.7440000000000001 -1.39 1 +-0.47200000000000003 0.267 1 +0.304 1.61 1 +-0.0439 0.305 1 +-1.96 -1.09 -1 +1.31 0.17 1 +-1.78 -0.253 -1 +-1.21 0.9179999999999999 -1 +0.457 0.191 1 +0.998 0.37 1 +0.513 1.5 1 +-0.7140000000000001 -0.161 1 +0.0722 1.8 1 +1.33 -0.0673 1 +0.508 -0.331 1 +0.862 -1.24 1 +0.65 1.86 1 +1.74 -0.14300000000000002 1 +0.41700000000000004 -1.54 1 +0.466 0.281 1 +1.73 0.9790000000000001 1 +-0.314 -0.72 1 +0.0981 0.465 1 +-1.08 1.01 -1 +2.37 1.07 1 +0.14300000000000002 -0.7859999999999999 1 +0.032 -1.5 1 +0.7390000000000001 2.14 1 +0.396 1.81 1 +0.5379999999999999 1.38 1 +0.0325 -1.46 1 +-0.391 1.32 1 +-0.695 1.08 -1 +-1.84 -0.846 -1 +-0.8240000000000001 1.1 -1 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